Accurate and recursive maps of crop and weeds at the field-scale could be of great interest with major economic and environmental impacts, including competition with native plant species, choking irrigation infrastructure, reducing agricultural yields and affecting the health of livestock.

The development of new generation multispectral satellite sensors and hyperspectral UAVs sensors has resulted in significant interest in their use for crop and weeds classification applications in view of their high spatial/spectral resolution. Small UAVs are more suited to site-specific weed management applications, as they can collect data at high spatial resolutions, which is essential for the classification of small or localized weed outbreaks. The extraction of features (spatial and/or spectral) that discriminate between the weeds of interest and background objects (i.e. different soils and crops) is crucial to map and monitor weeds in agricultural fields. Weed classification is an essential requirement for site-specific weed management in the context of precision agriculture, allowing a considerable reduction of herbicide spraying, with favorable environmental consequences. However, the spectral discrimination between crop and weeds from multi and hyperspectral remote sensing has yet to solve several issues, which is mainly due to the small spectral differences between crops of different species with also the requirement of a very high spatial resolution. Soil background effects are another problem that complicates weed detection in post-emergence row crops. Currently, no established methodology has been widely accepted, but different authors have employed successfully multivariate and machine learning algorithms such as PLSR-DA (Partial Least Squares Discriminant Analysis) and Support Vector Machine with a Radial Basis Function Kernel (Gaussian SVM) to discriminate and classify weeds from crops (Hermann et al., 2013; Hadoux et al., 2014) in using imaging spectroscopy field-based studies.

In the WP1 of the Topic 1, the main research activities are focused on: (a) setting up a spectral library of the most common crops and weeds in the investigated agricultural fields; (b) testing the capability of images acquired from both Sentinel 2 and UAVs imagery using different classification tools for crop mapping and weed detection; (c) mapping crops and weed patches in operational situations of site-specific crop management.

Performance of the applied crop and weeds classification methods are compared on Sentinel-2 and UAVs’ data sets related to agricultural fields in Central Italy. Classification performances that can be obtained depends not only on the scenario and, thus, data set, but also on the specific sensor and platform as well as on the classification methods employed.

Moreover, with global revisit times of five days from next months onwards, Sentinel-2 based classifications can probably be further improved by using (a) temporal information in addition to the spectral signatures and (b) textural as well as canopy height information from Sentinel-1 radar images.

As field measurement of massive germplasm for complex traits in breeding is challenging, there's a strong demand for real-time, fast and nondestructive phenotyping to accelerate the breeding efficiency. Unmanned aerial platforms‑based remote sensing can be used to rapidly and cost‑effectively phenotype large numbers of plots and field trials. Strategies for high-throughput field-based phenotyping in breeding was investigated by National Engineering Research Center for Information Technology in Agriculture (coordinated by NERCITA) in recent years, where proximal remote sensing is the deployment of sensors using aerial platforms. Strategies include the following: 1) Selection and specification of UAV platforms for application of phenotyping in breeding; 2) Rapid processing of multi-source remote sensing data for high-throughput phenotyping; 3) Analysis of phenotypic information for soybean, maize and wheat in breeding (over 10,000 plots) by proximal remote sensing under different growth periods; 4) Determining the optimal growth stage, indexes and algorithm model for crop yield prediction; 5) Validation of the phenotypic information resolution and yield prediction using agricultural UAV for breeding plots to ascertain its stability and accuracy; 6) Genome-wide association study of morphological indicators and genotype of maize and the identification of candidate gene.

Oral presentation

Exploitation of Multitemporal and Multisensor Earth Observation Data for Arable Crop Classification and Yield Assessment at the Farm Scale

Many studies have demonstrated the advantage of the exploitation of multi-temporal and multi-sensor remote sensing data for the improvement of crop classification methods. However the methods based on optical and SAR data for monitoring arable crops, such as cereals and forage crops (e.g. wheat, barley, oats, triticale, ryegrass...), both at the local and regional level, are still far from being effective and operational. This is because usually small differences in the canopy reflectance and SAR backscatter occurs among these crops, hampering a clear and robust discrimination. There is however, an increasing requirement, e.g. in the context of the European agricultural policy of "coupled subsidy" (payment related to real crop cultivation on parcel), to identify individual crops. For example in the "crop diversification" measure, imposed by the “Greening Policy”, it is necessary to separate barley and wheat on the same farm, which is rather difficult when using only a type of data. The objective of WP1 of the topic 1 of the Dragon4 project ID. 32275 "Combined Exploitation Of Sino EU Earth Observation Data for Supporting The Monitoring and Management of Agricultural Resources", is the development of classification algorithms based on multitemporal optical and radar data allowing the incorporation of cropping systems dynamics information. In this context, a study was carried out in the Maccarese farmland test site (Central Italy), in which a quite extensive dataset, including both optical (RapidEye, Landsat8 and Zy-Yuan 3) and radar (Cosmo SkyMed and Sentinel-1) data, was collected during the 2015 crop growth season. These data were used to test a multi-temporal phenology-based classification algorithm, based on pixel-level decision tree (DT) incorporating information on temporal crop dynamics. The results were validated using the ground data available within a farm management geographic information system (GIS).

The same dataset was also employed for field-based wheat yield estimation, which is the objective of WP5 of topic 1 of the mentioned Dragon4 project. For this purpose, the remote sensing data were converted into biophysical variables (LAI and above ground biomass), using empirical relationships with ground data obtained during the 2015 crop season, in field campaigns carried close to the satellite acquisitions (on wheat, barley, alfalfa, broad bean and maize). For optical data, an algorithm based on the training of artificial neural networks with PROSPECT+SAIL model simulations was employed to retrieve biophysical canopy variables. These variables were then assimilated into the SAFY model (Duchemin et al., 2008, Environ Model Softw. 23, 876-892), using the Ensemble Kalman Filter (EnKF) method, to estimate grain yield. The models were re-calibrated on the basis of a preliminary sensitivity analysis study. Two versions of the SAFY model were compared, i.e. the original SAFY and a modified version (SAFYE) which includes a description of the soil water balance and water stress factors (Veloso, 2014, PhD Thesis, University of Toulouse, France). The results were validated using yield map data collected using a yield monitor system available on the farm's combine harvester machine. A very small difference emerged between the results of the two model versions, suggesting that the original SAFY version, which includes less parameters and input variables than SAFYE, is a suitable and practical choice for farm-based grain yield estimation, although water stress is inferred indirectly, from the processes regulating leaf growth.

Oral presentation

Detection and Classification of Infestation Diseases by using multi- and hyper-spectral data

1Institute of Remote Sensing and Digital Earth, Chinese Academy of Science, China, People's Republic of; 2University of Chinese Academy of Sciences，Beijing, China; 3School of Aerospace Engineering, Sapienza University of Rome, Rome, Italy; 4Institute of Methodologies for Environmental Analysis, National Research Council of Italy, Rome, Italy

Yellow rust is one of the most important fungal diseases of wheat and have caused serious yield loss of winter wheat in China. Studies focused on detecting and monitoring of rust infestation highly rely on field investigation and visual judgement with an advanced pathogen attack. Automatic methods based on remotely sensed techniques and spectral absorption features for an early detection of rust disease are rarely discussed. On the other side, in Italy, the Province of Lecce, located in the region (Apulia) where the 35% of the Italian olive oil is produced has been significantly affected by the Xylella fastidiosa disease which caused a rapid decline in olive plantations, the so-called olive quick decline syndrome (OQDS, in Italian: complesso del disseccamento rapido dell'olivo). By the beginning of 2015 it had infected up to a million trees in the Lecce Province. Then this paper has two main objectives:

- determine the most sensitive spectral vegetation indices (SVIs) for characterizing specific pathological lesions of winter wheat leaves infected with rust disease, and then to propose a hyperspectral analysis procedure for the early detection and identification of rust diseases before specific symptoms became visible. Based on in situ hyperspectral data measured in experiments of 2002 and 2003 with a total of 122 samples, 14 pro-existing SVIs that are related to foliar physiological and photochemistry variations were assessed at different pathogen stages. And then, in order to extract the subtle spectral features between the healthy and diseased winter wheat leaves, an enhanced feature space (EFS) were developed by the non-linear combination and transformation of the identified SVIs. Based on this feature space, early differentiation of healthy leaves and leaves infected with yellow rust could be achieved by a Support Vector Machine (SVM) classifier. Finally, for validation and extension, this approach was also successfully implemented on MODIS data for mapping the rust occurrence conditions in the regional level in the major winter wheat planting area.

- presenting the preliminary results of the analysis of a time series of Landsat images of the Province of Lecce (Italy) spanning a period of seven years on the way to analyze the possibility of using available satellite images (e.g. L8 and Sentinel-2) datasets to assess the evolution of diseases on permanent crops (olive groves, vineyards), for example, the spread of phytosanitary threats as the Xylella fastidiosa (olive groves) or fungal trunk diseases (vineyards) in Italy.

Nitrogen (N) is an important indicator of the plant nutritional status and then affects the end of wheat production. Rapid real-time monitoring of wheat N status is crucial for precision N management during wheat growth. Traditional inversion methods by remote sensing are mostly based on its relationship with vegetation index or spectral band. Some models are not suitable as the change of time and location. N-PROSAIL model (Yang et al., 2016) was developed by replacing the absorption coefficient of chlorophyll in the original PROSAIL model (with an equivalent N absorption coefficient, and it gave us a physical method to estimate canopy nitrogen density (CND, g m-2). Therefore, the objective of this study was to estimate CND at different growth stages by inverting the N-PROSAIL model.

The inversion results with and without considering calibrating parameters at different growth stages were compared (Fig. 1). Without considering calibrating parameters at different growth stages (unified parameters at all growth stages, Li et al., 2015), The R2 and RMSE values for LAI and LND were 0.67 and 0.74, and 0.30 and 58.49 μg cm-2, respectively. Relationship between the simulated and measured CND showed well (R2 = 0.66, RMSE = 2.45 g m-2). After considering calibrating parameter at different growth stages, the estimation accuracy of LAI was improved (R2 =0.75, RMSE = 0.73), and LND estimation showed a significant improvement (R2 = 0.59, RMSE = 17.43 μg cm-2). In the end, the CND estimation performed better than CND estimation with unified parameter set at all growth stages, with R2 and RMSE values of 0.75 and 1.32 g m-2, respectively. These results confirm the potential of using N-PROSAIL model for CND retrieval in winter wheat at different growth stages and under variables climatic conditions.

Wheat powdery mildew is one of the serious crop diseases which affects the food safety of China. Integrating multi-source information (Earth Observation-EO, meteorological, etc.) to support decision making in the sustainable management of wheat powdery mildew in agriculture is demanded. In this study, the Landsat8 remote sensing image of May 22nd, 2014 was used to extract the land surface temperature (LST) and many vegetation indices including normalized difference vegetation index (NDVI), enhanced vegetation index(EVI), triangular vegetation index (TVI), wetness index and renormalized difference vegetation index (RDVI). Site daily meteorological data including temperature, humidity, rainfall, sunshine hour from April to May was used to get the parameters delineating the environment condition such as average temperature from April 22nd to May 22nd, average humidity from April 22nd to May 22nd, total sunshine hour from April 22nd to May 22nd and number of rainy days with more than 0.1 mm rainfall from April 22nd to May 22nd. Corresponding space meteorological features were got by combining remote sensing image and site daily meteorological data. In the field work, 148 field sites were collected in study area and the degree of wheat powdery mildew in these sites was recorded. An independent t-test analysis was used to test the difference between disease and healthy sites based on calibration data. Those vegetation indices and environmental factors that failed to show a statistical significant(p<0.001) were eliminated. Five variables including RDVI, temperature, humidity, average temperature from April 22nd to May 22nd and average humidity from April 22nd to May 22nd were identified as optimal explanatory variables for developing the powdery mildew forecasting model. The artificial neural network was trained using feedforward neural network learning algorithm in combination with simulated annealing technique to learn the relationship between selected factors and powdery mildew. The powdery mildew forecasting model based on artificial neural network (ANN) was established to predict powdery mildew occurrence of wheat in Gaocheng, Jinzhou and Zhaoxian County, Shijiazhuang City, Hebei Province. The results obtained from the ANN model were compared with prediction model developed using support vector machine(SVM) technique. The accuracy of models respectively based on validation samples were obtained to evaluate the difference of performance of the models. The result showed that the overall accuracy of the ANN model was 86.49%, which is higher than SVM model(78.38%). The result reveals that ANN model could be used to forecast the occurrence of wheat powdery mildew and compared with the traditional forecasting model based on support vector machine, ANN model has a better performance.

Crop yield is one of the most concerned complex traits in crop research, which is linked to CO2 fixation through the photosynthetic process and partitioning of photoassimilates to the harvested part of the plant. The traditional methods for measuring crop yield are the use of manual sampling or establishing the relationship between agronomic factors or climatic factors and crop yield using statistical analysis methods. Many observations and samplings in field experiments are required to determine the parameters of the yield prediction model, which is time-consuming, low efficiency, and incomplete spatial coverage. Unmanned aerial vehicle remote sensing platforms (UAV-RSPs) equipped with different sensors have recently become important means for fast and non-destructive access to complex traits and have the advantages of flexible and convenient operation, on-demand access to data and high spatial resolution, which provide a new way for studying phenomics and genomics. As improving the accuracy and adaptability of the yield estimation model is a prerequisite for the application of UAV remote sensingthe objective of the research is to build crop yield prediction models that combine crop physiology and remote sensing parameters to improve the accuracy of yield prediction by UAV-RSP.

Field experiment tested 98 breeding materials of soybean was conducted by NERCITA in 2015. Unmanned aerial platform equipped with digital camera, multi spectral camera and hyper-spectrometer was used for field-based high-throughput phenotyping of soybean in breeding plots. Ten vegetation indices (VIs) including NDVI, RVI, GNDVI, PVI, OSAVI, EVI, DVI and NDVI705 and plant height, combining algorithms including partial least-squares regression (PLS), multiple linear regression (MLR) and multiple stepwise regression (MSR) have been adopted for predicting yield of soybean in breeding plots. The results showed that MLR performed best, with R2, NRMSE and d values of 0.83, 6.61 and 0.95, respectively, while MSR had the lowest accuracy, with R2, NRMSE and d values of 0.72, 8.59 and 0.91, respectively.

The UAV-based field phenotyping platform can be as a preliminary method for screening cultivar in soybean breeding. The UAV platform equipped with multi-sensors was able to identify the differences of yields among the cultivars of soybean. Combing proximal sensing data and crop physiological traits can improve the accuracy of yield prediction in soybean breeding. The UAV-based proximal sensing platform provided novel insights in accelerating the breeding efficiency.

Leaf area index(LAI) and above ground biomass (AGB) are considered as two most important physiological parameters for crops, its correct estimation has been the focus in the crops growth monitoring and yield prediction. With the development of remote sensing technology and its application in agriculture, LAI and ABG estimation have been one of the main research areas of agricultural remote sensing. In recent years, unmanned aerial vehicle (UAV) technology has emerged as a new platform for remote sensing sensors, can provide remote sensing image in higher temporal resolution and spatial resolution. This study provides insight into the LAI and ABG estimation using an ASD Field Spec 3 spectrometer on the ground and applied to mapping using an UHD 185 hyperspectral sensor on board an UAV. The UAV was an 8-propellered UAV platform with 6 kg take-off weight, 50 meters flying height and 8m/s speed. The canopy spectral of winter wheat was measured by two methods, a DJI-S1000 UAV equipped with UHD 185 hyperspectral sensor fly 50m high, and a ASD Field Spec 3 spectrometer on the ground. By using 550nm, 680nm and 800nm spectral from ASD Field Spec 3 spectrometer, a linear model for LAI and ABG estimation was established. Results indicate that the R2 was 0.78 and 0.60, RMSE was 0.60m2/m2 and 1.46 t/ha, MAE was 0.48 m2/m2 and 1.19 t/ha, respectively. After the hyperspectral image fusion and mosaic processing, hyperspectral image of whole study area was obtained. We applied the models for LAI and ABG estimation on the hyperspectral image of whole study area, and completed the winter wheat LAI and ABG monitoring, with results of LAI and ABG as follows: R2 was 0.76 and 0.44, RMSE was 0.64 m2/m2 and 1.48 t/ha MAE was 0.54 m2/m2 and 1.15 t/ha. The results suggest the UAV UHD 185 hyperspectral system and the models have high application potential.